• 阿里云金融风控-贷款违约预测建模


    直接附上代码

    # -*- coding: utf-8 -*-
    """
    Created on Sat Jan 16 15:18:33 2021
    
    @author: Administrator
    """
    
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    import seaborn as sns
    import datetime
    import warnings
    warnings.filterwarnings('ignore')
    
    #%%导入数据
    data_train = pd.read_csv('D:/python_home/阿里云金融风控-贷款违约预测/train.csv')
    data_test_a = pd.read_csv('D:/python_home/阿里云金融风控-贷款违约预测/testA.csv')
    
    
    #%%基本的数据描述
    data_train.shape,data_test_a.shape  #((800000, 47), (200000, 46))
    
    data_train.columns
    '''
    Index(['id', 'loanAmnt', 'term', 'interestRate', 'installment', 'grade',
           'subGrade', 'employmentTitle', 'employmentLength', 'homeOwnership',
           'annualIncome', 'verificationStatus', 'issueDate', 'isDefault',
           'purpose', 'postCode', 'regionCode', 'dti', 'delinquency_2years',
           'ficoRangeLow', 'ficoRangeHigh', 'openAcc', 'pubRec',
           'pubRecBankruptcies', 'revolBal', 'revolUtil', 'totalAcc',
           'initialListStatus', 'applicationType', 'earliesCreditLine', 'title',
           'policyCode', 'n0', 'n1', 'n2', 'n3', 'n4', 'n5', 'n6', 'n7', 'n8',
           'n9', 'n10', 'n11', 'n12', 'n13', 'n14'],
          dtype='object')
    '''
    
    data_test_a.columns
    
    '''
    Index(['id', 'loanAmnt', 'term', 'interestRate', 'installment', 'grade',
           'subGrade', 'employmentTitle', 'employmentLength', 'homeOwnership',
           'annualIncome', 'verificationStatus', 'issueDate', 'purpose',
           'postCode', 'regionCode', 'dti', 'delinquency_2years', 'ficoRangeLow',
           'ficoRangeHigh', 'openAcc', 'pubRec', 'pubRecBankruptcies', 'revolBal',
           'revolUtil', 'totalAcc', 'initialListStatus', 'applicationType',
           'earliesCreditLine', 'title', 'policyCode', 'n0', 'n1', 'n2', 'n3',
           'n4', 'n5', 'n6', 'n7', 'n8', 'n9', 'n10', 'n11', 'n12', 'n13', 'n14'],
          dtype='object')
    '''
    
    #%%看一下变量的取值的个数,用来划分数值型还是类别型
    for i in list(data_train.columns):
        print(i, data_train[i].nunique())
    '''
    id 800000
    loanAmnt 1540  贷款金额
    term 2
    interestRate 641
    installment 72360
    grade 7
    subGrade 35
    employmentTitle 248683
    employmentLength 11
    homeOwnership 6
    annualIncome 44926
    verificationStatus 3
    issueDate 139
    isDefault 2
    purpose 14
    postCode 932
    regionCode 51
    dti 6321
    delinquency_2years 30
    ficoRangeLow 39
    ficoRangeHigh 39
    openAcc 75
    pubRec 32
    pubRecBankruptcies 11
    revolBal 71116
    revolUtil 1286
    totalAcc 134
    initialListStatus 2
    applicationType 2
    earliesCreditLine 720
    title 39644
    policyCode 1
    n0 39
    n1 33
    n2 50
    n3 50
    n4 46
    n5 65
    n6 107
    n7 70
    n8 102
    n9 44
    n10 76
    n11 5
    n12 5
    n13 28
    n14 31
    '''
    
    cate_col = ['term', 'grade',
           'subGrade', 'employmentLength', 'homeOwnership',
            'verificationStatus',  'isDefault',
           'purpose', 
           'pubRecBankruptcies', 
           'initialListStatus', 'applicationType', 
           'policyCode','n11', 'n12']
    
    num_col = [i for i in list(data_train.columns)[1:] if i not in cate_col]
    
    data_train[cate_col].nunique()
    
    
    #%%类别变量的iv值计算
    import pycard as pc
    cate_iv_woedf = pc.WoeDf()
    clf = pc.NumBin()
    for i in cate_col:
        cate_iv_woedf.append(pc.cross_woe(data_train[i] ,data_train.isDefault))
    cate_iv_woedf.to_excel('tmp11')
    
    cate_use_col = ['term','grade','verificationStatus']
    
    #%%数值型变量的iv值计算
    num_col.remove('issueDate')
    num_col.remove('earliesCreditLine')
    #上面这两个是时间日期的东西,后面再做处理吧
    
    num_iv_woedf = pc.WoeDf()
    clf = pc.NumBin()
    for i in num_col:
        clf.fit(data_train[i] ,data_train.isDefault)
        clf.generate_transform_fun()
        num_iv_woedf.append(clf.woe_df_)
    num_iv_woedf.to_excel('tmp12')
    
    from numpy import *
    data_train['loanAmnt_bin'] = pd.cut(data_train.loanAmnt,bins=[-inf, 3512.5, 9012.5,  10012.5, 11987.5, 15012.5, 28012.5, inf])
    
    #interestRate
    data_train['interestRate_bin'] = pd.cut(data_train.interestRate,bins=[-inf, 7.885, 9.73, 11.415, 13.175, 15.975, 17.785, 21.985, inf])
    
    #annualIncome
    data_train['annualIncome_bin'] = pd.cut(data_train.annualIncome,bins=[-inf, 37001.5996, 45670.5, 60995.5, 70017.5, 86462.0, 100670.5, 160030.0, inf])
    
    #dti,先用均值填充,再分
    data_train['dti'] = data_train['dti'].fillna(data_train['dti'].mean())
    data_train['dti_bin'] = pd.cut(data_train.dti,bins=[-inf, 10.745, 14.845, 18.255, 21.745, 25.325, 30.195, 33.225, inf])
    
    #ficoRangeLow
    data_train['ficoRangeLow_bin'] = pd.cut(data_train.ficoRangeLow,bins=[-inf, 667.5, 682.5, 692.5, 702.5, 717.5, 732.5, 767.5, inf])
    
    #revolUtil,均值填充,再分
    data_train['revolUtil'] = data_train['revolUtil'].fillna(data_train['revolUtil'].mean())
    data_train['revolUtil_bin'] = pd.cut(data_train.revolUtil,bins=[-inf, 19.75, 29.35, 38.55, 47.95, 56.55, 86.85, inf])
    
    #n14 空值作为一列,
    data_train['n14_bin'] = pd.cut(data_train.n14,bins=[-inf, 0.5, 1.5, 2.5, 3.5, 4.5, 6.5, inf])
    
    woe_col = [i for i in ['term','grade','verificationStatus']+list(data_train.columns)[-7:]]
    
    #%%
    
    
    cate_iv_woedf = pc.WoeDf()
    clf = pc.NumBin()
    for i in woe_col:
        cate_iv_woedf.append(pc.cross_woe(data_train[i] ,data_train.isDefault))
    cate_iv_woedf.to_excel('tmp11')
    
    data_train.grade[data_train.grade =='G'] = 'F'
    
    #%%woe转换
    pc.obj_info(cate_iv_woedf)
    
    cate_iv_woedf.bin2woe(data_train,woe_col)
    
    model_col = [i for i in ['id', 'isDefault']+list(data_train.columns)[-10:]]
    
    data_train[model_col].isnull().sum()
    data_train[model_col].info()
    model_data = data_train[model_col]
    model_data = model_data.astype(float)
    model_data.n14_woe[model_data.n14_woe.isnull()]=0.34984133
    
    #%%建模
    import pandas as pd
    import matplotlib.pyplot as plt #导入图像库
    import matplotlib
    import seaborn as sns
    import statsmodels.api as sm
    from sklearn.metrics import roc_curve, auc
    from sklearn.model_selection import train_test_split
    
    X = model_data[['_woe',
     'g_woe',
     'verificationSt_woe',
     'loanAmnt_woe',
     'interestRate_woe',
     'annualIncome_woe',
     'dti_woe',
     'ficoRangeLow_woe',
     'revolUtil_woe',
     'n14_woe']]
    Y = model_data['isDefault']
    
    
    x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.3,random_state=0)
    
    #(10127, 44)
    
    X1=sm.add_constant(x_train)   #在X前加上一列常数1,方便做带截距项的回归
    logit=sm.Logit(y_train.astype(float),X1.astype(float))
    result=logit.fit()
    result.summary()
    result.params
    
    
    
    X3 = sm.add_constant(x_test)
    resu = result.predict(X3.astype(float))
    fpr, tpr, threshold = roc_curve(y_test, resu)
    rocauc = auc(fpr, tpr)
    plt.plot(fpr, tpr, 'b', label='AUC = %0.2f' % rocauc)
    plt.legend(loc='lower right')
    plt.plot([0, 1], [0, 1], 'r--')
    plt.xlim([0, 1])
    plt.ylim([0, 1])
    plt.ylabel('真正率')
    plt.xlabel('假正率')
    plt.show()
    
    
    resu_1 = result.predict(X1.astype(float))
    fpr, tpr, threshold = roc_curve(y_train, resu_1)
    rocauc = auc(fpr, tpr)
    plt.plot(fpr, tpr, 'b', label='AUC = %0.2f' % rocauc)
    plt.legend(loc='lower right')
    plt.plot([0, 1], [0, 1], 'r--')
    plt.xlim([0, 1])
    plt.ylim([0, 1])
    plt.ylabel('真正率')
    plt.xlabel('假正率')
    plt.show()

     最后结果是 0.7026,比上次的0.6829 多了约2%,目前排名榜上最高分是0.7492,距离目标还差5%左右

    看了看别人写的代码,效果能达到7.348,现附上链接:https://blog.csdn.net/qq_44694861/article/details/109753004?spm=5176.12282029.0.0.209a4288OlvFjo

    代码如下:

    # -*- coding: utf-8 -*-
    """
    Created on Tue Feb  9 10:04:26 2021
    
    @author: Administrator
    """
    
    #%%
    import pandas as pd
    import datetime
    import warnings
    warnings.filterwarnings('ignore')
    from sklearn.model_selection import StratifiedKFold
    #warnings.filterwarnings('ignore')
    #%matplotlib inline
    from sklearn.metrics import roc_auc_score
    ## 数据降维处理的
    from sklearn.model_selection import train_test_split  
    from catboost import CatBoostClassifier
    
    #pip3 install --user  catboost -i https://pypi.tuna.tsinghua.edu.cn/simple/
    
    #%%
    train = pd.read_csv('D:/python_home/阿里云金融风控-贷款违约预测/train.csv')
    testA = pd.read_csv('D:/python_home/阿里云金融风控-贷款违约预测/testA.csv')
    
    
    #%%
    numerical_fea = list(train.select_dtypes(exclude=['object']).columns)
    numerical_fea.remove('isDefault')
    train[numerical_fea] = train[numerical_fea].fillna(train[numerical_fea].median())
    testA[numerical_fea] = testA[numerical_fea].fillna(testA[numerical_fea].median())
    #issueDate
    for data in [train]:
        data['issueDate'] = pd.to_datetime(data['issueDate'],format='%Y-%m-%d')
        data['grade'] = data['grade'].map({'A':1,'B':2,'C':3,'D':4,'E':5,'F':6,'G':7})
        data['employmentLength'] = data['employmentLength'].map({'1 year':1,'2 years':2,'3 years':3,'4 years':4,'5 years':5,'6 years':6,'7 years':7,'8 years':8,'9 years':9,'10+ years':10,'< 1 year':0})
        data['subGrade'] = data['subGrade'].map({'E2':1,'D2':2,'D3':3,'A4':4,'C2':5,'A5':6,'C3':7,'B4':8,'B5':9,'E5':10,
            'D4':11,'B3':12,'B2':13,'D1':14,'E1':15,'C5':16,'C1':17,'A2':18,'A3':19,'B1':20,
            'E3':21,'F1':22,'C4':23,'A1':24,'D5':25,'F2':26,'E4':27,'F3':28,'G2':29,'F5':30,
            'G3':31,'G1':32,'F4':33,'G4':34,'G5':35})
        data['earliesCreditLine'] = data['earliesCreditLine'].apply(lambda s: int(s[-4:]))
      #  data['n15']=data['n8']*data['n10']
        
    for data in [testA]:
        data['issueDate'] = pd.to_datetime(data['issueDate'],format='%Y-%m-%d')
        data['grade'] = data['grade'].map({'A':1,'B':2,'C':3,'D':4,'E':5,'F':6,'G':7})
        data['employmentLength'] = data['employmentLength'].map({'1 year':1,'2 years':2,'3 years':3,'4 years':4,'5 years':5,'6 years':6,'7 years':7,'8 years':8,'9 years':9,'10+ years':10,'< 1 year':0})
        data['subGrade'] = data['subGrade'].map({'E2':1,'D2':2,'D3':3,'A4':4,'C2':5,'A5':6,'C3':7,'B4':8,'B5':9,'E5':10,
            'D4':11,'B3':12,'B2':13,'D1':14,'E1':15,'C5':16,'C1':17,'A2':18,'A3':19,'B1':20,
            'E3':21,'F1':22,'C4':23,'A1':24,'D5':25,'F2':26,'E4':27,'F3':28,'G2':29,'F5':30,
            'G3':31,'G1':32,'F4':33,'G4':34,'G5':35})
        data['earliesCreditLine'] = data['earliesCreditLine'].apply(lambda s: int(s[-4:]))
    
    print("数据预处理完成!")  
    
    #%%
    sub=testA[['id']].copy()
    sub['isDefault']=0
    testA=testA.drop(['id','issueDate'],axis=1)
    data_x=train.drop(['isDefault','id','issueDate'],axis=1)
    data_y=train[['isDefault']].copy()
    x, val_x, y, val_y = train_test_split(  
        data_x,  
        data_y,  
        test_size=0.25,  
        random_state=1,  
        stratify=data_y
    )  
    
    col=['grade','subGrade','employmentTitle','homeOwnership','verificationStatus','purpose','postCode','regionCode',
         'initialListStatus','applicationType','policyCode']
    for i in data_x.columns:
        if i in col:
            data_x[i] = data_x[i].astype('str')
    for i in testA.columns:
        if i in col:
            testA[i] = testA[i].astype('str')
    
    #%%
    model=CatBoostClassifier(
                loss_function="Logloss",
                eval_metric="AUC",
                task_type="CPU",
                learning_rate=0.1,
                iterations=500,
                random_seed=2020,
                od_type="Iter",
                depth=7)
    
    answers = []
    mean_score = 0
    n_folds = 5
    sk = StratifiedKFold(n_splits=n_folds, shuffle=True, random_state=2019)
    for train, test in sk.split(data_x, data_y):
        x_train = data_x.iloc[train]
        y_train = data_y.iloc[train]
        x_test = data_x.iloc[test]
        y_test = data_y.iloc[test]
        clf = model.fit(x_train,y_train, eval_set=(x_test,y_test),verbose=500,cat_features=col)
        yy_pred_valid=clf.predict(x_test)
        print('cat验证的auc:{}'.format(roc_auc_score(y_test, yy_pred_valid)))
        mean_score += roc_auc_score(y_test, yy_pred_valid) / n_folds
        y_pred_valid = clf.predict(testA,prediction_type='Probability')[:,-1]
        answers.append(y_pred_valid)
    print('mean valAuc:{}'.format(mean_score))
    
    #%%
    cat_pre=sum(answers)/n_folds
    sub['isDefault']=cat_pre
    sub.to_csv('金融预测.csv',index=False)

    注意事项:

    1.catboost只能识别字符类型和数值类型的数据

    2.代码需要很长的时间去跑

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  • 原文地址:https://www.cnblogs.com/cgmcoding/p/14287004.html
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